Back to Search
Start Over
Augmented Reality Assisted Assembly Training Oriented Dynamic Gesture Recognition and Prediction
- Source :
- Applied Sciences, Volume 11, Issue 21, Applied Sciences, Vol 11, Iss 9789, p 9789 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- Augmented reality assisted assembly training (ARAAT) is an effective and affordable technique for labor training in the automobile and electronic industry. In general, most tasks of ARAAT are conducted by real-time hand operations. In this paper, we propose an algorithm of dynamic gesture recognition and prediction that aims to evaluate the standard and achievement of the hand operations for a given task in ARAAT. We consider that the given task can be decomposed into a series of hand operations and furthermore each hand operation into several continuous actions. Then, each action is related with a standard gesture based on the practical assembly task such that the standard and achievement of the actions included in the operations can be identified and predicted by the sequences of gestures instead of the performance throughout the whole task. Based on the practical industrial assembly, we specified five typical tasks, three typical operations, and six standard actions. We used Zernike moments combined histogram of oriented gradient and linear interpolation motion trajectories to represent 2D static and 3D dynamic features of standard gestures, respectively, and chose the directional pulse-coupled neural network as the classifier to recognize the gestures. In addition, we defined an action unit to reduce the dimensions of features and computational cost. During gesture recognition, we optimized the gesture boundaries iteratively by calculating the score probability density distribution to reduce interferences of invalid gestures and improve precision. The proposed algorithm was evaluated on four datasets and proved to increase recognition accuracy and reduce the computational cost from the experimental results.
- Subjects :
- Technology
QH301-705.5
Computer science
QC1-999
Linear interpolation
Task (project management)
Histogram
Classifier (linguistics)
General Materials Science
Computer vision
Biology (General)
augmented reality assisted assembly training
QD1-999
Instrumentation
human-machine interaction
Fluid Flow and Transfer Processes
Artificial neural network
business.industry
Physics
Process Chemistry and Technology
General Engineering
gesture recognition and prediction
Engineering (General). Civil engineering (General)
Computer Science Applications
Chemistry
Gesture recognition
Augmented reality
Artificial intelligence
TA1-2040
business
Gesture
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....b3018823d5530326039c94f8930c7f8c
- Full Text :
- https://doi.org/10.3390/app11219789